Codonfm Finetune

Fine-tune public Encodon

Use --pretrained_ckpt_path for public v1. Do not substitute --checkpoint_path: the public runner does not forward that argument to the fine-tuning task.

Instructions

Resolve the target label, dataset, checkpoint, and output directory from the request and available files. Reuse existing data and weights. For training, check the project's ML dependencies and a compatible NVIDIA GPU before launch. If a required resource is unavailable, complete the available data preparation and return the command with the missing prerequisite clearly identified. When training is requested and the prerequisites are met, execute it and check the resulting checkpoints and metrics. A request for preparation ends with the validated inputs and command.

Use the user's labeled dataset when provided. For a demonstration of sequence regression without a dataset, use the public human RiboNN translation-efficiency data below and state that choice. This is not a substitute for a user's intended assay or for labeled coding variants. If variant labels are missing, return the required schema and a command template promptly; do not search for labels or invent measured effects.

Default demonstration checkpoint: nvidia/NV-CodonFM-Encodon-80M-v1, revision 399ca9fe17b57941a7bebc6788033919b417413c, file NV-CodonFM-Encodon-80M-v1.safetensors with sibling config.json. The public weights are about 307 MB. Download them when needed for the requested work; input preparation can record an intended checkpoint path. These are the original Encodon weights; the -TE- checkpoints use the separate TransformerEngine implementation.

For an unsupported Decodon or missense-aggregation request, inspect the public parser/model configuration, explain the missing feature, and finish. Do not implement the missing model or search private repositories.

Examples

Prepare a small public-data example with the standard-library helper prepare_ribonn.py, running from the repository root. Set CODONFM_DATA_PATH to the CSV you want to create:

python skills/codonfm-finetune/scripts/prepare_ribonn.py \
    --output "$CODONFM_DATA_PATH"

With an existing raw file, add --input "$RIBONN_DATA_PATH". The default reads at most eight accepted rows per split; --max-rows-per-split 0 processes the full input. Remote streaming has a time budget and no automatic retries; use a local file if it fails. The helper follows the CDS slicing in the RiboNN notebook:

• Read the upstream .csv with a tab delimiter.
• Set ref_seq = tx_sequence[utr5_size:utr5_size + cds_size], id = transcript_id, and value = mean_te unchanged. Do not take another logarithm.
• Preserve source fold groups: 0–7 become train, 8 becomes val, 9 becomes test. This is a demonstration holdout, not the notebook's cross-validation.
• Exclude invalid/non-finite rows and CDSs exceeding 2046 codons instead of silently truncating labeled examples. Record counts and source in the adjacent .metadata.json. A small subset does not establish predictive performance.

The pinned dataset URL is in the helper; its source is CenikLab/TE_classic_ML. The notebook extracts frozen Encodon embeddings and trains a random-forest regressor with fold-based cross-validation. This skill reuses its data source, CDS extraction, and target for a separate fine-tuning example; it does not reproduce the notebook's training procedure or results.

Supported strategies

• lora: adapter fine-tuning; default choice for smaller datasets.
• head_only_random: freeze the backbone and train a new head.
• head_only_pretrained: train an existing compatible pretrained head.
• full: update the complete model.

Accept only encodon_80m, encodon_600m, or encodon_1b.

Sequence-level regression or classification

Require id, ref_seq, value, and split columns. Extra columns are allowed. Map the user's columns to this loader schema; the RiboNN helper is only for RiboNN source data. Training needs train rows and, when validation is enabled, val rows. A test split is needed only for later evaluation. Labels in unused splits need not be populated. Regression targets must be finite numbers; classification targets must be integer class indices from zero through num_classes - 1. Use a downstream head for scalar targets.

Check sequence preparation with the user’s assay in mind. The loader converts uppercase RNA U to T, and the tokenizer uppercases bases. Ambiguous bases and incomplete codons can produce unknown tokens; overlength sequences are truncated. Review these cases rather than silently dropping user records. Choose batches and a training budget appropriate to the dataset; small training sets may be resampled by the loader.

Set CODONFM_CHECKPOINT_PATH to the checkpoint file and CODONFM_RUN_DIR to your chosen output directory. This example runs ten steps to check the workflow; choose the training budget for the actual dataset and task:

python -m src.runner finetune \
    --exp_name property_finetune \
    --model_name encodon_80m \
    --pretrained_ckpt_path "$CODONFM_CHECKPOINT_PATH" \
    --data_path "$CODONFM_DATA_PATH" \
    --process_item codon_sequence \
    --dataset_name CodonBertDataset \
    --finetune_strategy lora \
    --lora_alpha 32 \
    --lora_r 16 \
    --lora_dropout 0.1 \
    --loss_type regression \
    --use_downstream_head \
    --lr 2e-5 \
    --max_steps 10 \
    --warmup_iterations 1 \
    --check_val_every_n_epoch 1 \
    --train_batch_size 4 \
    --val_batch_size 4 \
    --num_workers 0 \
    --num_nodes 1 \
    --num_gpus 1 \
    --out_dir "$CODONFM_RUN_DIR" \
    --checkpoints_dir "$CODONFM_RUN_DIR/checkpoints"

For classification, replace --loss_type regression with --loss_type classification and pass the correct --num_classes.